BioStats Quick Calculator
Quickly perform t-test, chi-square, or ANOVA calculations on your data. Results are instant and calculated entirely in your browser.
Calculation Results
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Detailed Results Table
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Understanding Common BioStats Tests
Choosing the right statistical test is crucial for drawing valid conclusions from your biological data. Here's a brief overview of the tests available in this calculator:
Student's t-test
The t-test is used to compare the means of two groups. It's ideal when you want to determine if there is a significant difference between the averages of two independent samples (e.g., control vs. treatment group).
- Assumptions: Data should be normally distributed, and variances should be roughly equal (though variations like Welch's t-test can handle unequal variances).
- Input: Two sets of numerical data.
- Output: t-statistic, degrees of freedom, and p-value.
Chi-square Test (χ²)
The chi-square test is used to determine if there is a significant association between two categorical variables. It compares observed frequencies with expected frequencies. It's commonly used in genetics (e.g., Mendelian ratios), ecology (e.g., species distribution), or epidemiology (e.g., disease prevalence across categories).
- Assumptions: Data must be frequencies or counts, not percentages or ratios. Expected frequencies should not be too small (typically, no more than 20% of expected counts are less than 5).
- Input: Observed frequencies organized in a contingency table (e.g., enter counts per category for each group).
- Output: Chi-square statistic, degrees of freedom, and p-value.
One-way ANOVA (Analysis of Variance)
ANOVA is used to compare the means of three or more independent groups. It determines if there is a statistically significant difference between the means of these groups. If ANOVA shows a significant difference, post-hoc tests (not included in this simple calculator) are typically used to identify which specific groups differ from each other.
- Assumptions: Data should be normally distributed within each group, and variances should be roughly equal across groups.
- Input: Three or more sets of numerical data.
- Output: F-statistic, degrees of freedom, and p-value.
P-value Interpretation: The p-value helps you determine the significance of your results. A commonly used threshold is 0.05. If your p-value is less than 0.05, it suggests that the observed differences are statistically significant, meaning they are unlikely to have occurred by random chance.